Mass transit systems, such as subways and busses, often have varying ridership patterns, such as on different days of the week, different months of the year, and during different seasons. However, individual events, such as weather events, holidays, concerts, sporting events, etc. can cause the ridership of a transit system on a particular day, time, and/or route to vary significantly. Thus, a particular transit vehicle, such as a subway train departing a particular subway station at a particular time, may itself have varying numbers of passengers, and therefore, varying crowdedness levels. For example, a particular subway train may experience reduced or increased crowdedness due to a confluence of events, thereby making it difficult for an individual passenger to know whether or not a particular transit vehicle is likely to be crowded or not.
In order to get a better understanding of general ridership patterns, some transit system operators may choose to determine average transit vehicle crowdedness levels. For example, a subway system operator may choose one week per year to weigh individual transit vehicles to determine relative ridership patterns on various transit routes. However, such ridership patterns tend to reflect rough approximations of vehicle crowdedness, and do not take into account individual anomalies which can cause fluctuations in the crowdedness of any given transit vehicle at any given time.
Further, the equipment used to obtain such general ridership patterns can be expensive and difficult to retrofit into an existing transit station. Moreover, while some transit system operators may count the number of passengers entering or exiting various transit stations, such gate data does not indicate which specific transit vehicles the passengers choose to ride, and by extension, the real-time crowdedness of the specific transit vehicles.